----------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/singh/Google Drive/PLS/Research/Compulsory Voting/Compulsion, Abstention, and Spoiled Ballots/PSRM Replication Materials/P
> SRM Replication Log, Singh.log
  log type:  text
 opened on:  16 Sep 2016, 16:01:50

. do "/var/folders/63/8fq62ffn6sng8s9wbw2p6m74rd1twr/T//SD27849.000000"

. ******                  
. ****This code replicates the models in the paper "Politically Unengaged, Distrusting, and Disaffected Individuals Drive the Link Between Com
> pulsory Voting and Invalid Balloting", by Shane P. Singh, which appears in Political Science Research and Methods.
. ******  
. set seed 123

. version 13              

. 
end of do-file

. use "/Users/singh/Google Drive/PLS/Research/Compulsory Voting/Compulsion, Abstention, and Spoiled Ballots/PSRM Replication Materials/PSRM Re
> plication Data, Singh.dta"
(All data are copyrighted by LAPOP. For more info, run the command note list)

. do "/var/folders/63/8fq62ffn6sng8s9wbw2p6m74rd1twr/T//SD27849.000000"

. 
. ******
. *Install necessary add-ons
. ******
. ssc install rd
checking rd consistency and verifying not already installed...

the following files already exist and are different:
    /Users/singh/Library/Application Support/Stata/ado/plus/r/rd.ado
    /Users/singh/Library/Application Support/Stata/ado/plus/r/rd.hlp

no files installed or copied
(no action taken)
r(602);

end of do-file

r(602);

. do "/var/folders/63/8fq62ffn6sng8s9wbw2p6m74rd1twr/T//SD27849.000000"

. net from http://www.stata.com/users/vwiggins
----------------------------------------------------------------------------------------------------------------------------------------------
http://www.stata.com/users/vwiggins/
Materials by Vince Wiggins, StataCorp
----------------------------------------------------------------------------------------------------------------------------------------------

DIRECTORIES you could -net cd- to:
    ..                back to other contributors

PACKAGES you could -net describe-:
    biprobit2         accepts citer(#) option and may improve convergence
    boston04          graphics talk given at 3rd NASUG meeting
    finirr            computes the internal rate of return of a cash stream
    grand2            presents indicators in grand mean form after -fit-
    grc1leg           combine graphs into one graph with a common legend.
    labsave5          saves labels to a do file in Stata 5 format
    postmiss          modification of post to allow expressions to be missing
    uk04              graphics talk given at 10th UK Users Group Meeting
----------------------------------------------------------------------------------------------------------------------------------------------

. net install grc1leg
checking grc1leg consistency and verifying not already installed...
all files already exist and are up to date.

. 
end of do-file

. do "/var/folders/63/8fq62ffn6sng8s9wbw2p6m74rd1twr/T//SD27849.000000"

. 
. ******  
. *Create variable that will limit the sample to observations that are not missing on the key covariates. Exclude people living in countries w
> ith compulsory voting that are above or below age enforcement thresholds, and exlude observations in the USA and Canada and other countries 
> that were not originally in the AmericasBarometer Grand Merged File
. ******  
. destring year, replace
year already numeric; no replace

. gen ageCV = .
(157358 missing values generated)

. label var ageCV "person was of CV age in Ecuador, Peru, Argentina, Bolivia, or Brazil"

. replace ageCV = 1 if country == "Ecuador"  | country == "Peru"  | country == "Argentina"| country ==  "Brazil"  | country ==  "Bolivia" 
(36688 real changes made)

. replace ageCV = 0 if country == "Ecuador" & age>64 | country == "Peru" & age>69 | country == "Argentina" & age>69 | country ==  "Brazil" & a
> ge>69 | country ==  "Brazil" & age<18 | country ==  "Bolivia" & age>69
(2021 real changes made)

. replace ageCV = 0 if country == "Ecuador" & age<18 & year > 2008
(0 real changes made)

. replace ageCV = 0 if country == "Argentina" & age<18 & year > 2011
(0 real changes made)

. replace ageCV = 0 if country == "Bolivia"  & age<21 
(1216 real changes made)

. 
. reg would_spoil_or_blank age_10 educ    GDPpercapita_PPP polity_Bel cpi if ageCV ~= 0 & country~= "United States" & country~= "Canada" & cou
> ntry~= "Suriname" & country~= "Trinidad & Tobago" , cl(countryandyear)

Linear regression                                      Number of obs =   76232
                                                       F(  5,    48) =   13.60
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0068
                                                       Root MSE      =  .28002

                            (Std. Err. adjusted for 49 clusters in countryandyear)
----------------------------------------------------------------------------------
                 |               Robust
would_spoil_or~k |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
          age_10 |  -.0093636     .00139    -6.74   0.000    -.0121584   -.0065689
            educ |   .0120412    .009899     1.22   0.230    -.0078621    .0319446
GDPpercapita_PPP |   .0033959   .0027263     1.25   0.219    -.0020858    .0088776
      polity_Bel |  -.0085522   .0076707    -1.11   0.270    -.0239752    .0068707
             cpi |  -.0118735   .0083341    -1.42   0.161    -.0286303    .0048833
           _cons |    .238536    .107402     2.22   0.031     .0225898    .4544821
----------------------------------------------------------------------------------

. gen samp_CVspoil_noUSCAN = 1 if e(sample)
(81126 missing values generated)

. 
. 
. 
. ******                  
. ****Multilevel Ordered Logit Models and Associated Figures. Use "difficult" and/or nonadaptive quadrature ("intmethod(ghermite)") options to
>  facilitate convergence.
. ******
. 
. **
. *Model 1
. **
. xtlogit would_spoil_or_blank  b0.CVscale_Payne  age_10 educ  GDPpercapita_PPP polity_Bel cpi if samp_CVspoil_noUSCAN == 1, i(cntryyearnum) i
> ntmethod(ghermite)

Fitting comparison model:

Iteration 0:   log likelihood = -22423.324  
Iteration 1:   log likelihood = -21334.088  
Iteration 2:   log likelihood = -21181.258  
Iteration 3:   log likelihood = -21181.104  
Iteration 4:   log likelihood = -21181.104  

Fitting full model:

tau =  0.0     log likelihood = -21181.104
tau =  0.1     log likelihood = -20472.059
tau =  0.2     log likelihood = -20459.792
tau =  0.3     log likelihood = -20471.282

Iteration 0:   log likelihood = -20459.792  (not concave)
Iteration 1:   log likelihood = -20453.024  (not concave)
Iteration 2:   log likelihood = -20452.265  (not concave)
Iteration 3:   log likelihood = -20449.594  (not concave)
Iteration 4:   log likelihood = -20448.277  (not concave)
Iteration 5:   log likelihood = -20447.254  (not concave)
Iteration 6:   log likelihood = -20446.706  (not concave)
Iteration 7:   log likelihood = -20446.427  (not concave)
Iteration 8:   log likelihood = -20445.831  (not concave)
Iteration 9:   log likelihood = -20445.764  (not concave)
Iteration 10:  log likelihood = -20445.706  (not concave)
Iteration 11:  log likelihood =  -20445.65  
Iteration 12:  log likelihood = -20445.062  (backed up)
Iteration 13:  log likelihood = -20444.309  
Iteration 14:  log likelihood =   -20444.3  
Iteration 15:  log likelihood =   -20444.3  

Random-effects logistic regression              Number of obs      =     76232
Group variable: cntryyearnum                    Number of groups   =        49

Random effects u_i ~ Gaussian                   Obs per group: min =      1166
                                                               avg =    1555.8
                                                               max =      2744

Integration method: ghermite                    Integration points =        12

                                                Wald chi2(8)       =   1242.61
Log likelihood  =   -20444.3                    Prob > chi2        =    0.0000

--------------------------------------------------------------------------------------
would_spoil_or_blank |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
       CVscale_Payne |
                  1  |  -.1212606   .1034495    -1.17   0.241     -.324018    .0814968
                  2  |    .904843   .0655272    13.81   0.000      .776412    1.033274
                  3  |   1.540072   .0571388    26.95   0.000     1.428082    1.652062
                     |
              age_10 |  -.1144855   .0093446   -12.25   0.000    -.1328006   -.0961704
                educ |   -.062627   .0454257    -1.38   0.168    -.1516596    .0264057
    GDPpercapita_PPP |   .0251336   .0080296     3.13   0.002     .0093959    .0408713
          polity_Bel |  -.0351169   .0125897    -2.79   0.005    -.0597923   -.0104416
                 cpi |   .0414144   .0212639     1.95   0.051    -.0002621    .0830909
               _cons |  -2.966083   .2559266   -11.59   0.000     -3.46769   -2.464476
---------------------+----------------------------------------------------------------
            /lnsig2u |  -1.380202   .0671856                     -1.511883   -1.248521
---------------------+----------------------------------------------------------------
             sigma_u |   .5015254   .0168477                      .4695682    .5356575
                 rho |    .071025   .0044329                      .0628124    .0802195
--------------------------------------------------------------------------------------
Likelihood-ratio test of rho=0: chibar2(01) =  1473.61 Prob >= chibar2 = 0.000

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
-------------+---------------------------------------------------------------
           . |  76232           .    -20444.3     10      40908.6    41001.02
-----------------------------------------------------------------------------
               Note:  N=Obs used in calculating BIC; see [R] BIC note

. 
. **
. *Figure 1
. **
. margins, over(CVscale_Payne) atmeans predict(pu0)

Adjusted predictions                              Number of obs   =      76232
Model VCE    : OIM

Expression   : Pr(would_spoil_or_blank=1 assuming u_i=0), predict(pu0)
over         : CVscale_Payne
at           : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   age_10          =    3.820027 (mean)
                   educ            =     .056661 (mean)
                   GDPpercapi~P    =    5.425804 (mean)
                   polity_Bel      =    6.759798 (mean)
                   cpi             =    7.244364 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   age_10          =    3.950542 (mean)
                   educ            =     .080078 (mean)
                   GDPpercapi~P    =     8.38264 (mean)
                   polity_Bel      =    8.528606 (mean)
                   cpi             =    6.225688 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   age_10          =    3.753365 (mean)
                   educ            =    .0673515 (mean)
                   GDPpercapi~P    =     8.32475 (mean)
                   polity_Bel      =    7.761513 (mean)
                   cpi             =    6.939604 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   age_10          =    3.936211 (mean)
                   educ            =    .1313688 (mean)
                   GDPpercapi~P    =    9.079609 (mean)
                   polity_Bel      =    7.766627 (mean)
                   cpi             =    5.852635 (mean)

-------------------------------------------------------------------------------
              |            Delta-method
              |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
CVscale_Payne |
           0  |   .0388672   .0020907    18.59   0.000     .0347695     .042965
           1  |   .0330662   .0028941    11.43   0.000     .0273939    .0387386
           2  |   .0935454   .0024994    37.43   0.000     .0886468    .0984441
           3  |   .1561668   .0026735    58.41   0.000     .1509269    .1614067
-------------------------------------------------------------------------------

. marginsplot, ///
>      recast(scatter) recastci(rcap) scheme(s1color)  title("") ///
>          ci1opts(color(green*.5) lwidth(medthick))  /// 
>      plot1opts(mcolor(green)) ///  
>          xlabel(0 "VV{subscript: }" 1 "CV{subscript:low}" 2 "CV{subscript:med}" 3 "CV{subscript:high}")   ///
>      xtitle(Level of Compulsory Voting, margin(medsmall))  ytitle(Pr(Blank or Spoiled Ballot))  aspectratio(1.25) 

  Variables that uniquely identify margins: CVscale_Payne

. 
. 
.          
. **
. *Model 2
. **
. melogit would_spoil_or_blank  b0.CVscale_Payne##c.ignor_disint_scale_cent  age_10 educ  GDPpercapita_PPP polity_Bel cpi ignor_disint_scale_m
> ean if samp_CVspoil_noUSCAN == 1  || countryandyear: ignor_disint_scale_cent,  intpoints(11)  difficult   intmethod(ghermite) 

Fitting fixed-effects model:

Iteration 0:   log likelihood = -20873.176  
Iteration 1:   log likelihood = -19935.499  
Iteration 2:   log likelihood = -19926.423  
Iteration 3:   log likelihood = -19926.411  
Iteration 4:   log likelihood = -19926.411  

Refining starting values:

Grid node 0:   log likelihood = -19516.477

Refining starting values (unscaled likelihoods):

Grid node 0:   log likelihood = -19516.477

Fitting full model:

Iteration 0:   log likelihood = -19516.477  (not concave)
Iteration 1:   log likelihood = -19493.297  (not concave)
Iteration 2:   log likelihood =  -19431.06  (not concave)
Iteration 3:   log likelihood = -19404.581  (not concave)
Iteration 4:   log likelihood = -19369.888  (not concave)
Iteration 5:   log likelihood = -19341.564  (not concave)
Iteration 6:   log likelihood = -19324.398  
Iteration 7:   log likelihood = -19319.416  
Iteration 8:   log likelihood = -19315.182  
Iteration 9:   log likelihood = -19314.591  
Iteration 10:  log likelihood = -19314.523  
Iteration 11:  log likelihood = -19314.523  

Mixed-effects logistic regression               Number of obs      =     72352
Group variable:  countryandyear                 Number of groups   =        49

                                                Obs per group: min =      1138
                                                               avg =    1476.6
                                                               max =      2630

Integration method:    ghermite                 Integration points =        11

                                                Wald chi2(13)      =   2045.71
Log likelihood = -19314.523                     Prob > chi2        =    0.0000
---------------------------------------------------------------------------------------------------------
                   would_spoil_or_blank |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
----------------------------------------+----------------------------------------------------------------
                                        |
                          CVscale_Payne |
                                     1  |  -.0865418   .0661111    -1.31   0.191    -.2161171    .0430335
                                     2  |   .5362844   .0646371     8.30   0.000     .4095979    .6629708
                                     3  |   1.325956   .0566909    23.39   0.000     1.214844    1.437068
                                        |
                ignor_disint_scale_cent |   .1936713   .0626685     3.09   0.002     .0708433    .3164992
                                        |
CVscale_Payne#c.ignor_disint_scale_cent |
                                     1  |   .1301366   .0789616     1.65   0.099    -.0246253    .2848984
                                     2  |   .0425625   .0765622     0.56   0.578    -.1074966    .1926217
                                     3  |   .2475207    .073882     3.35   0.001     .1027145    .3923268
                                        |
                                 age_10 |  -.1129976   .0096168   -11.75   0.000    -.1318462    -.094149
                                   educ |   .1101957   .0468017     2.35   0.019      .018466    .2019253
                       GDPpercapita_PPP |   .0827025   .0075334    10.98   0.000     .0679372    .0974678
                             polity_Bel |  -.1050575   .0128346    -8.19   0.000    -.1302129   -.0799022
                                    cpi |  -.0558031   .0194416    -2.87   0.004    -.0939079   -.0176982
                ignor_disint_scale_mean |   1.054477   .1226285     8.60   0.000     .8141292    1.294824
                                  _cons |  -5.080736   .3764132   -13.50   0.000    -5.818492   -4.342979
----------------------------------------+----------------------------------------------------------------
countryandyear                          |
            var(ignor_disint_scale_cent)|   .0098911   .0050284                      .0036519    .0267904
                              var(_cons)|   .4630921   .0319811                      .4044674     .530214
---------------------------------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =  1223.78   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
-------------+---------------------------------------------------------------
           . |  72352           .   -19314.52     16     38661.05    38808.07
-----------------------------------------------------------------------------
               Note:  N=Obs used in calculating BIC; see [R] BIC note

. margins, over(CVscale_Payne) at(ignor_disint_scale_cent = (-2 -1 0 1 2)) atmeans predict(mu fixedonly)

Adjusted predictions                              Number of obs   =      72352
Model VCE    : OIM

Expression   : Predicted mean, fixed portion only, predict(mu fixedonly)
over         : CVscale_Payne

1._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   ignor_disi~t    =          -2
                   age_10          =    3.811343 (mean)
                   educ            =    .0590044 (mean)
                   GDPpercapi~P    =    5.442561 (mean)
                   polity_Bel      =    6.765413 (mean)
                   cpi             =    7.236676 (mean)
                   ignor_disi~n    =    2.815063 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   ignor_disi~t    =          -2
                   age_10          =    3.932666 (mean)
                   educ            =    .0828147 (mean)
                   GDPpercapi~P    =    8.421058 (mean)
                   polity_Bel      =    8.530206 (mean)
                   cpi             =    6.216616 (mean)
                   ignor_disi~n    =    2.776193 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   ignor_disi~t    =          -2
                   age_10          =    3.744914 (mean)
                   educ            =    .0697724 (mean)
                   GDPpercapi~P    =    8.350295 (mean)
                   polity_Bel      =    7.767125 (mean)
                   cpi             =    6.935098 (mean)
                   ignor_disi~n    =    2.903588 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   ignor_disi~t    =          -2
                   age_10          =    3.926314 (mean)
                   educ            =    .1342779 (mean)
                   GDPpercapi~P    =     9.11397 (mean)
                   polity_Bel      =    7.782966 (mean)
                   cpi             =    5.836983 (mean)
                   ignor_disi~n    =    2.800144 (mean)

2._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   ignor_disi~t    =          -1
                   age_10          =    3.811343 (mean)
                   educ            =    .0590044 (mean)
                   GDPpercapi~P    =    5.442561 (mean)
                   polity_Bel      =    6.765413 (mean)
                   cpi             =    7.236676 (mean)
                   ignor_disi~n    =    2.815063 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   ignor_disi~t    =          -1
                   age_10          =    3.932666 (mean)
                   educ            =    .0828147 (mean)
                   GDPpercapi~P    =    8.421058 (mean)
                   polity_Bel      =    8.530206 (mean)
                   cpi             =    6.216616 (mean)
                   ignor_disi~n    =    2.776193 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   ignor_disi~t    =          -1
                   age_10          =    3.744914 (mean)
                   educ            =    .0697724 (mean)
                   GDPpercapi~P    =    8.350295 (mean)
                   polity_Bel      =    7.767125 (mean)
                   cpi             =    6.935098 (mean)
                   ignor_disi~n    =    2.903588 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   ignor_disi~t    =          -1
                   age_10          =    3.926314 (mean)
                   educ            =    .1342779 (mean)
                   GDPpercapi~P    =     9.11397 (mean)
                   polity_Bel      =    7.782966 (mean)
                   cpi             =    5.836983 (mean)
                   ignor_disi~n    =    2.800144 (mean)

3._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   ignor_disi~t    =           0
                   age_10          =    3.811343 (mean)
                   educ            =    .0590044 (mean)
                   GDPpercapi~P    =    5.442561 (mean)
                   polity_Bel      =    6.765413 (mean)
                   cpi             =    7.236676 (mean)
                   ignor_disi~n    =    2.815063 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   ignor_disi~t    =           0
                   age_10          =    3.932666 (mean)
                   educ            =    .0828147 (mean)
                   GDPpercapi~P    =    8.421058 (mean)
                   polity_Bel      =    8.530206 (mean)
                   cpi             =    6.216616 (mean)
                   ignor_disi~n    =    2.776193 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   ignor_disi~t    =           0
                   age_10          =    3.744914 (mean)
                   educ            =    .0697724 (mean)
                   GDPpercapi~P    =    8.350295 (mean)
                   polity_Bel      =    7.767125 (mean)
                   cpi             =    6.935098 (mean)
                   ignor_disi~n    =    2.903588 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   ignor_disi~t    =           0
                   age_10          =    3.926314 (mean)
                   educ            =    .1342779 (mean)
                   GDPpercapi~P    =     9.11397 (mean)
                   polity_Bel      =    7.782966 (mean)
                   cpi             =    5.836983 (mean)
                   ignor_disi~n    =    2.800144 (mean)

4._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   ignor_disi~t    =           1
                   age_10          =    3.811343 (mean)
                   educ            =    .0590044 (mean)
                   GDPpercapi~P    =    5.442561 (mean)
                   polity_Bel      =    6.765413 (mean)
                   cpi             =    7.236676 (mean)
                   ignor_disi~n    =    2.815063 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   ignor_disi~t    =           1
                   age_10          =    3.932666 (mean)
                   educ            =    .0828147 (mean)
                   GDPpercapi~P    =    8.421058 (mean)
                   polity_Bel      =    8.530206 (mean)
                   cpi             =    6.216616 (mean)
                   ignor_disi~n    =    2.776193 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   ignor_disi~t    =           1
                   age_10          =    3.744914 (mean)
                   educ            =    .0697724 (mean)
                   GDPpercapi~P    =    8.350295 (mean)
                   polity_Bel      =    7.767125 (mean)
                   cpi             =    6.935098 (mean)
                   ignor_disi~n    =    2.903588 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   ignor_disi~t    =           1
                   age_10          =    3.926314 (mean)
                   educ            =    .1342779 (mean)
                   GDPpercapi~P    =     9.11397 (mean)
                   polity_Bel      =    7.782966 (mean)
                   cpi             =    5.836983 (mean)
                   ignor_disi~n    =    2.800144 (mean)

5._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   ignor_disi~t    =           2
                   age_10          =    3.811343 (mean)
                   educ            =    .0590044 (mean)
                   GDPpercapi~P    =    5.442561 (mean)
                   polity_Bel      =    6.765413 (mean)
                   cpi             =    7.236676 (mean)
                   ignor_disi~n    =    2.815063 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   ignor_disi~t    =           2
                   age_10          =    3.932666 (mean)
                   educ            =    .0828147 (mean)
                   GDPpercapi~P    =    8.421058 (mean)
                   polity_Bel      =    8.530206 (mean)
                   cpi             =    6.216616 (mean)
                   ignor_disi~n    =    2.776193 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   ignor_disi~t    =           2
                   age_10          =    3.744914 (mean)
                   educ            =    .0697724 (mean)
                   GDPpercapi~P    =    8.350295 (mean)
                   polity_Bel      =    7.767125 (mean)
                   cpi             =    6.935098 (mean)
                   ignor_disi~n    =    2.903588 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   ignor_disi~t    =           2
                   age_10          =    3.926314 (mean)
                   educ            =    .1342779 (mean)
                   GDPpercapi~P    =     9.11397 (mean)
                   polity_Bel      =    7.782966 (mean)
                   cpi             =    5.836983 (mean)
                   ignor_disi~n    =    2.800144 (mean)

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
_at#CVscale_Payne |
             1 0  |   .0269015   .0036794     7.31   0.000       .01969     .034113
             1 1  |   .0204453   .0021815     9.37   0.000     .0161697    .0247209
             1 2  |   .0529986   .0049143    10.78   0.000     .0433667    .0626304
             1 3  |   .0756498   .0058676    12.89   0.000     .0641495      .08715
             2 0  |   .0324636   .0027179    11.94   0.000     .0271366    .0377907
             2 1  |    .028044   .0018476    15.18   0.000     .0244227    .0316653
             2 2  |   .0661864   .0035516    18.64   0.000     .0592254    .0731474
             2 3  |    .112867   .0046909    24.06   0.000      .103673     .122061
             3 0  |   .0391296   .0020846    18.77   0.000     .0350438    .0432154
             3 1  |   .0383562   .0016132    23.78   0.000     .0351944    .0415179
             3 2  |   .0823703   .0022591    36.46   0.000     .0779425    .0867982
             3 3  |   .1651231   .0028608    57.72   0.000     .1595161    .1707302
             4 0  |   .0470976   .0036226    13.00   0.000     .0399975    .0541977
             4 1  |   .0522564   .0030605    17.07   0.000      .046258    .0582549
             4 2  |    .102079   .0044537    22.92   0.000       .09335    .1108081
             4 3  |   .2351599   .0075205    31.27   0.000       .22042    .2498999
             5 0  |   .0565927   .0071254     7.94   0.000     .0426272    .0705581
             5 1  |   .0708232   .0066828    10.60   0.000      .057725    .0839213
             5 2  |   .1258567   .0096571    13.03   0.000     .1069291    .1447842
             5 3  |   .3233959   .0171568    18.85   0.000     .2897693    .3570225
-----------------------------------------------------------------------------------

. marginsplot, ///
>      recast(scatter) recastci(rcap) scheme(s1color)  title("") ///
>          ci1opts(color(pink*.5) lwidth(medthick))  /// 
>      plot1opts(mcolor(pink))  ///        
>          ci2opts(color(red*.5) lwidth(medthick))  /// 
>      plot2opts(mcolor(red))  ///  
>          ci3opts(color(blue*.5) lwidth(medthick))  /// 
>      plot3opts(mcolor(blue))  ///  
>          ci4opts(color(black*.5) lwidth(medthick))  /// 
>      plot4opts(mcolor(black))  ///  
>      xtitle(Lack of Political Information and Interest)  ytitle("Pr(Blank or Spoiled Ballot)")  ///
>          legend(order(5 "VV" 6 "CV{subscript:low}" 7 "CV{subscript:med}" 8 "CV{subscript:high}") rows(1)) aspect(1) ///
>          xlabel(-2 "1" -1 "2" 0 "3" 1 "4" 2 "5") ///
>          name(ignor_disint, replace)

  Variables that uniquely identify margins: ignor_disint_scale_cent CVscale_Payne

. 
.          
. **
. *Model 3
. **
. melogit would_spoil_or_blank  b0.CVscale_Payne##c.distrust_scale_cent  age_10 educ  GDPpercapita_PPP polity_Bel cpi distrust_scale_mean if s
> amp_CVspoil_noUSCAN == 1  || countryandyear: distrust_scale_cent,  intpoints(14)     intmethod(ghermite) iterate(25) 

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -13881.21  
Iteration 1:   log likelihood = -13245.543  
Iteration 2:   log likelihood = -13236.409  
Iteration 3:   log likelihood = -13236.362  
Iteration 4:   log likelihood = -13236.362  

Refining starting values:

Grid node 0:   log likelihood =  -13343.58

Refining starting values (unscaled likelihoods):

Grid node 0:   log likelihood =  -13343.58

Fitting full model:

Iteration 0:   log likelihood =  -13343.58  (not concave)
Iteration 1:   log likelihood = -13057.428  (not concave)
Iteration 2:   log likelihood = -12942.217  (not concave)
Iteration 3:   log likelihood = -12873.001  (not concave)
Iteration 4:   log likelihood = -12869.787  (not concave)
Iteration 5:   log likelihood = -12866.795  
Iteration 6:   log likelihood = -12866.778  (backed up)
Iteration 7:   log likelihood = -12847.107  (not concave)
Iteration 8:   log likelihood = -12847.073  (not concave)
Iteration 9:   log likelihood = -12846.476  
Iteration 10:  log likelihood =  -12846.47  
Iteration 11:  log likelihood = -12846.412  
Iteration 12:  log likelihood = -12846.412  

Mixed-effects logistic regression               Number of obs      =     47625
Group variable:  countryandyear                 Number of groups   =        32

                                                Obs per group: min =      1115
                                                               avg =    1488.3
                                                               max =      2633

Integration method:    ghermite                 Integration points =        14

                                                Wald chi2(13)      =   1252.88
Log likelihood = -12846.412                     Prob > chi2        =    0.0000
-----------------------------------------------------------------------------------------------------
               would_spoil_or_blank |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                                    |
                      CVscale_Payne |
                                 1  |  -.2963336   .1127646    -2.63   0.009    -.5173482   -.0753191
                                 2  |     1.1473    .089439    12.83   0.000     .9720032    1.322597
                                 3  |    1.88669    .087083    21.67   0.000     1.716011     2.05737
                                    |
                distrust_scale_cent |   .0016837   .0602218     0.03   0.978    -.1163488    .1197162
                                    |
CVscale_Payne#c.distrust_scale_cent |
                                 1  |   .1043378   .0722582     1.44   0.149    -.0372857    .2459614
                                 2  |   .2098519   .0706383     2.97   0.003     .0714034    .3483003
                                 3  |    .274414   .0677086     4.05   0.000     .1417076    .4071203
                                    |
                             age_10 |  -.1108552   .0120399    -9.21   0.000     -.134453   -.0872574
                               educ |  -.0796401    .055184    -1.44   0.149    -.1877988    .0285185
                   GDPpercapita_PPP |   .0150349   .0087639     1.72   0.086    -.0021421    .0322118
                         polity_Bel |   .0427906   .0224571     1.91   0.057    -.0012244    .0868057
                                cpi |   .1169485   .0365581     3.20   0.001     .0452961     .188601
                distrust_scale_mean |  -.0902205    .047903    -1.88   0.060    -.1841087    .0036677
                              _cons |   -4.11576    .378306   -10.88   0.000    -4.857226   -3.374294
------------------------------------+----------------------------------------------------------------
countryandyear                      |
            var(distrust_scale_cent)|   .0073567   .0029706                       .003334    .0162329
                          var(_cons)|   .7732662   .0766593                      .6367126    .9391061
-----------------------------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =   779.90   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
-------------+---------------------------------------------------------------
           . |  47625           .   -12846.41     16     25724.82    25865.16
-----------------------------------------------------------------------------
               Note:  N=Obs used in calculating BIC; see [R] BIC note

. margins, over(CVscale_Payne) at(distrust_scale_cent = (-3 -1.5 0 1.5 3))  atmeans predict(mu fixedonly)

Adjusted predictions                              Number of obs   =      47625
Model VCE    : OIM

Expression   : Predicted mean, fixed portion only, predict(mu fixedonly)
over         : CVscale_Payne

1._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   distrust_s~t    =          -3
                   age_10          =     3.72396 (mean)
                   educ            =    .0673451 (mean)
                   GDPpercapi~P    =    6.021575 (mean)
                   polity_Bel      =    7.636336 (mean)
                   cpi             =    7.426729 (mean)
                   distrust_s~n    =    4.590712 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   distrust_s~t    =          -3
                   age_10          =    3.889463 (mean)
                   educ            =    .0874865 (mean)
                   GDPpercapi~P    =    8.107785 (mean)
                   polity_Bel      =    8.491814 (mean)
                   cpi             =    6.275586 (mean)
                   distrust_s~n    =    4.153076 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   distrust_s~t    =          -3
                   age_10          =    3.697283 (mean)
                   educ            =     .073671 (mean)
                   GDPpercapi~P    =    8.566868 (mean)
                   polity_Bel      =    7.781649 (mean)
                   cpi             =    7.010663 (mean)
                   distrust_s~n    =    4.413681 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   distrust_s~t    =          -3
                   age_10          =    3.888752 (mean)
                   educ            =    .1351975 (mean)
                   GDPpercapi~P    =    8.676265 (mean)
                   polity_Bel      =    7.678224 (mean)
                   cpi             =    6.038875 (mean)
                   distrust_s~n    =    4.226764 (mean)

2._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   distrust_s~t    =        -1.5
                   age_10          =     3.72396 (mean)
                   educ            =    .0673451 (mean)
                   GDPpercapi~P    =    6.021575 (mean)
                   polity_Bel      =    7.636336 (mean)
                   cpi             =    7.426729 (mean)
                   distrust_s~n    =    4.590712 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   distrust_s~t    =        -1.5
                   age_10          =    3.889463 (mean)
                   educ            =    .0874865 (mean)
                   GDPpercapi~P    =    8.107785 (mean)
                   polity_Bel      =    8.491814 (mean)
                   cpi             =    6.275586 (mean)
                   distrust_s~n    =    4.153076 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   distrust_s~t    =        -1.5
                   age_10          =    3.697283 (mean)
                   educ            =     .073671 (mean)
                   GDPpercapi~P    =    8.566868 (mean)
                   polity_Bel      =    7.781649 (mean)
                   cpi             =    7.010663 (mean)
                   distrust_s~n    =    4.413681 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   distrust_s~t    =        -1.5
                   age_10          =    3.888752 (mean)
                   educ            =    .1351975 (mean)
                   GDPpercapi~P    =    8.676265 (mean)
                   polity_Bel      =    7.678224 (mean)
                   cpi             =    6.038875 (mean)
                   distrust_s~n    =    4.226764 (mean)

3._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   distrust_s~t    =           0
                   age_10          =     3.72396 (mean)
                   educ            =    .0673451 (mean)
                   GDPpercapi~P    =    6.021575 (mean)
                   polity_Bel      =    7.636336 (mean)
                   cpi             =    7.426729 (mean)
                   distrust_s~n    =    4.590712 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   distrust_s~t    =           0
                   age_10          =    3.889463 (mean)
                   educ            =    .0874865 (mean)
                   GDPpercapi~P    =    8.107785 (mean)
                   polity_Bel      =    8.491814 (mean)
                   cpi             =    6.275586 (mean)
                   distrust_s~n    =    4.153076 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   distrust_s~t    =           0
                   age_10          =    3.697283 (mean)
                   educ            =     .073671 (mean)
                   GDPpercapi~P    =    8.566868 (mean)
                   polity_Bel      =    7.781649 (mean)
                   cpi             =    7.010663 (mean)
                   distrust_s~n    =    4.413681 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   distrust_s~t    =           0
                   age_10          =    3.888752 (mean)
                   educ            =    .1351975 (mean)
                   GDPpercapi~P    =    8.676265 (mean)
                   polity_Bel      =    7.678224 (mean)
                   cpi             =    6.038875 (mean)
                   distrust_s~n    =    4.226764 (mean)

4._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   distrust_s~t    =         1.5
                   age_10          =     3.72396 (mean)
                   educ            =    .0673451 (mean)
                   GDPpercapi~P    =    6.021575 (mean)
                   polity_Bel      =    7.636336 (mean)
                   cpi             =    7.426729 (mean)
                   distrust_s~n    =    4.590712 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   distrust_s~t    =         1.5
                   age_10          =    3.889463 (mean)
                   educ            =    .0874865 (mean)
                   GDPpercapi~P    =    8.107785 (mean)
                   polity_Bel      =    8.491814 (mean)
                   cpi             =    6.275586 (mean)
                   distrust_s~n    =    4.153076 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   distrust_s~t    =         1.5
                   age_10          =    3.697283 (mean)
                   educ            =     .073671 (mean)
                   GDPpercapi~P    =    8.566868 (mean)
                   polity_Bel      =    7.781649 (mean)
                   cpi             =    7.010663 (mean)
                   distrust_s~n    =    4.413681 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   distrust_s~t    =         1.5
                   age_10          =    3.888752 (mean)
                   educ            =    .1351975 (mean)
                   GDPpercapi~P    =    8.676265 (mean)
                   polity_Bel      =    7.678224 (mean)
                   cpi             =    6.038875 (mean)
                   distrust_s~n    =    4.226764 (mean)

5._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   distrust_s~t    =           3
                   age_10          =     3.72396 (mean)
                   educ            =    .0673451 (mean)
                   GDPpercapi~P    =    6.021575 (mean)
                   polity_Bel      =    7.636336 (mean)
                   cpi             =    7.426729 (mean)
                   distrust_s~n    =    4.590712 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   distrust_s~t    =           3
                   age_10          =    3.889463 (mean)
                   educ            =    .0874865 (mean)
                   GDPpercapi~P    =    8.107785 (mean)
                   polity_Bel      =    8.491814 (mean)
                   cpi             =    6.275586 (mean)
                   distrust_s~n    =    4.153076 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   distrust_s~t    =           3
                   age_10          =    3.697283 (mean)
                   educ            =     .073671 (mean)
                   GDPpercapi~P    =    8.566868 (mean)
                   polity_Bel      =    7.781649 (mean)
                   cpi             =    7.010663 (mean)
                   distrust_s~n    =    4.413681 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   distrust_s~t    =           3
                   age_10          =    3.888752 (mean)
                   educ            =    .1351975 (mean)
                   GDPpercapi~P    =    8.676265 (mean)
                   polity_Bel      =    7.678224 (mean)
                   cpi             =    6.038875 (mean)
                   distrust_s~n    =    4.226764 (mean)

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
_at#CVscale_Payne |
             1 0  |   .0249083   .0049944     4.99   0.000     .0151194    .0346973
             1 1  |   .0130762   .0020356     6.42   0.000     .0090865     .017066
             1 2  |   .0416732    .004947     8.42   0.000     .0319772    .0513692
             1 3  |   .0620754   .0058449    10.62   0.000     .0506197    .0735312
             2 0  |   .0249697   .0031069     8.04   0.000     .0188802    .0310593
             2 1  |   .0152958     .00173     8.84   0.000      .011905    .0186866
             2 2  |   .0563577   .0038481    14.65   0.000     .0488156    .0638999
             2 3  |   .0910259   .0046841    19.43   0.000     .0818452    .1002065
             3 0  |   .0250313   .0019834    12.62   0.000     .0211438    .0289188
             3 1  |   .0178853   .0016275    10.99   0.000     .0146954    .0210751
             3 2  |   .0758074   .0026651    28.44   0.000     .0705839    .0810309
             3 3  |   .1315839   .0031725    41.48   0.000      .125366    .1378018
             4 0  |    .025093   .0028154     8.91   0.000     .0195749    .0306111
             4 1  |   .0209038   .0021519     9.71   0.000     .0166861    .0251215
             4 2  |   .1012492   .0057398    17.64   0.000     .0899994    .1124989
             4 3  |   .1865053   .0079932    23.33   0.000     .1708388    .2021717
             5 0  |   .0251549    .004666     5.39   0.000     .0160098       .0343
             5 1  |   .0244191   .0034196     7.14   0.000     .0177169    .0311213
             5 2  |   .1339916   .0131437    10.19   0.000     .1082304    .1597527
             5 3  |   .2575516   .0183603    14.03   0.000      .221566    .2935371
-----------------------------------------------------------------------------------

. marginsplot, ///
>      recast(scatter) recastci(rcap) scheme(s1color)  title("") ///
>          ci1opts(color(pink*.5) lwidth(medthick))  /// 
>      plot1opts(mcolor(pink))  ///        
>          ci2opts(color(red*.5) lwidth(medthick))  /// 
>      plot2opts(mcolor(red))  ///  
>          ci3opts(color(blue*.5) lwidth(medthick))  /// 
>      plot3opts(mcolor(blue))  ///  
>          ci4opts(color(black*.5) lwidth(medthick))  /// 
>      plot4opts(mcolor(black))  ///  
>      xtitle(Political Distrust)  ytitle("Pr(Blank or Spoiled Ballot)")  ///
>          legend(order(5 "VV" 6 "CV{subscript:low}" 7 "CV{subscript:med}" 8 "CV{subscript:high}") rows(1)) aspect(1) ///
>          xlabel(-3 "1" -1.5 "1.5" 0 "3" 1.5 "4.5" 3 "7") ///
>          name(distrust, replace)

  Variables that uniquely identify margins: distrust_scale_cent CVscale_Payne

.         
.         
.         
. **
. *Model 4
. **
. melogit would_spoil_or_blank  b0.CVscale_Payne##c.negative_orient_dem_scale_cent  age_10 educ  GDPpercapita_PPP polity_Bel cpi negative_orie
> nt_dem_scale_mean if samp_CVspoil_noUSCAN == 1  || countryandyear: negative_orient_dem_scale_cent,  intpoints(11)     intmethod(ghermite) 

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -16290.43  
Iteration 1:   log likelihood = -15557.799  
Iteration 2:   log likelihood = -15550.286  
Iteration 3:   log likelihood = -15550.269  
Iteration 4:   log likelihood = -15550.269  

Refining starting values:

Grid node 0:   log likelihood = -15221.172

Refining starting values (unscaled likelihoods):

Grid node 0:   log likelihood = -15221.172

Fitting full model:

Iteration 0:   log likelihood = -15221.172  (not concave)
Iteration 1:   log likelihood = -15205.745  (not concave)
Iteration 2:   log likelihood = -15145.228  (not concave)
Iteration 3:   log likelihood = -15130.908  (not concave)
Iteration 4:   log likelihood = -15126.883  
Iteration 5:   log likelihood = -15109.946  
Iteration 6:   log likelihood = -15090.702  
Iteration 7:   log likelihood = -15086.894  (not concave)
Iteration 8:   log likelihood = -15080.304  
Iteration 9:   log likelihood = -15078.407  
Iteration 10:  log likelihood = -15078.256  
Iteration 11:  log likelihood = -15078.256  

Mixed-effects logistic regression               Number of obs      =     56717
Group variable:  countryandyear                 Number of groups   =        49

                                                Obs per group: min =       564
                                                               avg =    1157.5
                                                               max =      2521

Integration method:    ghermite                 Integration points =        11

                                                Wald chi2(13)      =   1737.47
Log likelihood = -15078.256                     Prob > chi2        =    0.0000
----------------------------------------------------------------------------------------------------------------
                          would_spoil_or_blank |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------------------------+----------------------------------------------------------------
                                               |
                                 CVscale_Payne |
                                            1  |   .2845144   .0740805     3.84   0.000     .1393192    .4297096
                                            2  |   1.111538   .0736831    15.09   0.000     .9671217    1.255954
                                            3  |   1.893388   .0664759    28.48   0.000     1.763098    2.023679
                                               |
                negative_orient_dem_scale_cent |   .1272853   .0920552     1.38   0.167    -.0531395    .3077101
                                               |
CVscale_Payne#c.negative_orient_dem_scale_cent |
                                            1  |   .0033442   .1159621     0.03   0.977    -.2239374    .2306257
                                            2  |   .0838063   .1126639     0.74   0.457    -.1370108    .3046235
                                            3  |   .2663741   .1179187     2.26   0.024     .0352578    .4974905
                                               |
                                        age_10 |   -.116507   .0110561   -10.54   0.000    -.1381765   -.0948375
                                          educ |  -.0784364   .0510163    -1.54   0.124    -.1784265    .0215537
                              GDPpercapita_PPP |   .0207875   .0072052     2.89   0.004     .0066656    .0349094
                                    polity_Bel |  -.0053643   .0157199    -0.34   0.733    -.0361748    .0254462
                                           cpi |   .0562597   .0245587     2.29   0.022     .0081255    .1043939
                negative_orient_dem_scale_mean |   .1997151   .1073397     1.86   0.063    -.0106668     .410097
                                         _cons |  -3.915984   .3176567   -12.33   0.000     -4.53858   -3.293388
-----------------------------------------------+----------------------------------------------------------------
countryandyear                                 |
            var(negative_orient_dem_scale_cent)|   .0367095   .0149816                      .0164966     .081689
                                     var(_cons)|   .7085897   .0520769                      .6135316    .8183758
----------------------------------------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(2) =   944.03   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
-------------+---------------------------------------------------------------
           . |  56717           .   -15078.26     16     30188.51    30331.65
-----------------------------------------------------------------------------
               Note:  N=Obs used in calculating BIC; see [R] BIC note

. margins, over(CVscale_Payne) at(negative_orient_dem_scale_cent = (-2 -1 0 1 2)) atmeans predict(mu fixedonly)

Adjusted predictions                              Number of obs   =      56717
Model VCE    : OIM

Expression   : Predicted mean, fixed portion only, predict(mu fixedonly)
over         : CVscale_Payne

1._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   negative_o~t    =          -2
                   age_10          =    3.788954 (mean)
                   educ            =    .0611659 (mean)
                   GDPpercapi~P    =    5.475127 (mean)
                   polity_Bel      =    7.173351 (mean)
                   cpi             =    7.288743 (mean)
                   negative_o~n    =    2.850896 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   negative_o~t    =          -2
                   age_10          =    3.916659 (mean)
                   educ            =    .0894891 (mean)
                   GDPpercapi~P    =    8.367951 (mean)
                   polity_Bel      =    8.530649 (mean)
                   cpi             =    6.221202 (mean)
                   negative_o~n    =     2.80821 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   negative_o~t    =          -2
                   age_10          =    3.706032 (mean)
                   educ            =    .0746551 (mean)
                   GDPpercapi~P    =    8.384423 (mean)
                   polity_Bel      =    7.767717 (mean)
                   cpi             =     6.97809 (mean)
                   negative_o~n    =    2.951872 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   negative_o~t    =          -2
                   age_10          =    3.908512 (mean)
                   educ            =    .1385843 (mean)
                   GDPpercapi~P    =    8.920448 (mean)
                   polity_Bel      =    7.755985 (mean)
                   cpi             =    5.913437 (mean)
                   negative_o~n    =    2.721117 (mean)

2._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   negative_o~t    =          -1
                   age_10          =    3.788954 (mean)
                   educ            =    .0611659 (mean)
                   GDPpercapi~P    =    5.475127 (mean)
                   polity_Bel      =    7.173351 (mean)
                   cpi             =    7.288743 (mean)
                   negative_o~n    =    2.850896 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   negative_o~t    =          -1
                   age_10          =    3.916659 (mean)
                   educ            =    .0894891 (mean)
                   GDPpercapi~P    =    8.367951 (mean)
                   polity_Bel      =    8.530649 (mean)
                   cpi             =    6.221202 (mean)
                   negative_o~n    =     2.80821 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   negative_o~t    =          -1
                   age_10          =    3.706032 (mean)
                   educ            =    .0746551 (mean)
                   GDPpercapi~P    =    8.384423 (mean)
                   polity_Bel      =    7.767717 (mean)
                   cpi             =     6.97809 (mean)
                   negative_o~n    =    2.951872 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   negative_o~t    =          -1
                   age_10          =    3.908512 (mean)
                   educ            =    .1385843 (mean)
                   GDPpercapi~P    =    8.920448 (mean)
                   polity_Bel      =    7.755985 (mean)
                   cpi             =    5.913437 (mean)
                   negative_o~n    =    2.721117 (mean)

3._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   negative_o~t    =           0
                   age_10          =    3.788954 (mean)
                   educ            =    .0611659 (mean)
                   GDPpercapi~P    =    5.475127 (mean)
                   polity_Bel      =    7.173351 (mean)
                   cpi             =    7.288743 (mean)
                   negative_o~n    =    2.850896 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   negative_o~t    =           0
                   age_10          =    3.916659 (mean)
                   educ            =    .0894891 (mean)
                   GDPpercapi~P    =    8.367951 (mean)
                   polity_Bel      =    8.530649 (mean)
                   cpi             =    6.221202 (mean)
                   negative_o~n    =     2.80821 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   negative_o~t    =           0
                   age_10          =    3.706032 (mean)
                   educ            =    .0746551 (mean)
                   GDPpercapi~P    =    8.384423 (mean)
                   polity_Bel      =    7.767717 (mean)
                   cpi             =     6.97809 (mean)
                   negative_o~n    =    2.951872 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   negative_o~t    =           0
                   age_10          =    3.908512 (mean)
                   educ            =    .1385843 (mean)
                   GDPpercapi~P    =    8.920448 (mean)
                   polity_Bel      =    7.755985 (mean)
                   cpi             =    5.913437 (mean)
                   negative_o~n    =    2.721117 (mean)

4._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   negative_o~t    =           1
                   age_10          =    3.788954 (mean)
                   educ            =    .0611659 (mean)
                   GDPpercapi~P    =    5.475127 (mean)
                   polity_Bel      =    7.173351 (mean)
                   cpi             =    7.288743 (mean)
                   negative_o~n    =    2.850896 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   negative_o~t    =           1
                   age_10          =    3.916659 (mean)
                   educ            =    .0894891 (mean)
                   GDPpercapi~P    =    8.367951 (mean)
                   polity_Bel      =    8.530649 (mean)
                   cpi             =    6.221202 (mean)
                   negative_o~n    =     2.80821 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   negative_o~t    =           1
                   age_10          =    3.706032 (mean)
                   educ            =    .0746551 (mean)
                   GDPpercapi~P    =    8.384423 (mean)
                   polity_Bel      =    7.767717 (mean)
                   cpi             =     6.97809 (mean)
                   negative_o~n    =    2.951872 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   negative_o~t    =           1
                   age_10          =    3.908512 (mean)
                   educ            =    .1385843 (mean)
                   GDPpercapi~P    =    8.920448 (mean)
                   polity_Bel      =    7.755985 (mean)
                   cpi             =    5.913437 (mean)
                   negative_o~n    =    2.721117 (mean)

5._at        : 0.CVscale_Payne
                   CVscale_Payne   =           0
                   negative_o~t    =           2
                   age_10          =    3.788954 (mean)
                   educ            =    .0611659 (mean)
                   GDPpercapi~P    =    5.475127 (mean)
                   polity_Bel      =    7.173351 (mean)
                   cpi             =    7.288743 (mean)
                   negative_o~n    =    2.850896 (mean)
               1.CVscale_Payne
                   CVscale_Payne   =           1
                   negative_o~t    =           2
                   age_10          =    3.916659 (mean)
                   educ            =    .0894891 (mean)
                   GDPpercapi~P    =    8.367951 (mean)
                   polity_Bel      =    8.530649 (mean)
                   cpi             =    6.221202 (mean)
                   negative_o~n    =     2.80821 (mean)
               2.CVscale_Payne
                   CVscale_Payne   =           2
                   negative_o~t    =           2
                   age_10          =    3.706032 (mean)
                   educ            =    .0746551 (mean)
                   GDPpercapi~P    =    8.384423 (mean)
                   polity_Bel      =    7.767717 (mean)
                   cpi             =     6.97809 (mean)
                   negative_o~n    =    2.951872 (mean)
               3.CVscale_Payne
                   CVscale_Payne   =           3
                   negative_o~t    =           2
                   age_10          =    3.908512 (mean)
                   educ            =    .1385843 (mean)
                   GDPpercapi~P    =    8.920448 (mean)
                   polity_Bel      =    7.755985 (mean)
                   cpi             =    5.913437 (mean)
                   negative_o~n    =    2.721117 (mean)

-----------------------------------------------------------------------------------
                  |            Delta-method
                  |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
_at#CVscale_Payne |
             1 0  |   .0275979    .005333     5.17   0.000     .0171455    .0380504
             1 1  |   .0349915   .0050349     6.95   0.000     .0251233    .0448596
             1 2  |   .0724586   .0095773     7.57   0.000     .0536875    .0912297
             1 3  |    .094817    .013668     6.94   0.000     .0680282    .1216057
             2 0  |   .0312271   .0034646     9.01   0.000     .0244366    .0380176
             2 1  |   .0396806   .0032103    12.36   0.000     .0333885    .0459727
             2 2  |   .0879898   .0063854    13.78   0.000     .0754746     .100505
             2 3  |   .1344085   .0096332    13.95   0.000     .1155278    .1532892
             3 0  |   .0353162   .0020693    17.07   0.000     .0312604     .039372
             3 1  |   .0449688    .001825    24.64   0.000      .041392    .0485457
             3 2  |   .1064679   .0034376    30.97   0.000     .0997302    .1132056
             3 3  |   .1871145   .0035764    52.32   0.000     .1801048    .1941242
             4 0  |   .0399187   .0040579     9.84   0.000     .0319653    .0478721
             4 1  |   .0509245   .0038535    13.21   0.000     .0433717    .0584773
             4 2  |   .1282806   .0080862    15.86   0.000      .112432    .1441293
             4 3  |   .2544135   .0152365    16.70   0.000     .2245505    .2842764
             5 0  |    .045093   .0081337     5.54   0.000     .0291513    .0610347
             5 1  |   .0576213   .0078223     7.37   0.000     .0422898    .0729528
             5 2  |   .1537931   .0175092     8.78   0.000     .1194757    .1881105
             5 3  |   .3359152   .0349548     9.61   0.000     .2674051    .4044254
-----------------------------------------------------------------------------------

. marginsplot, ///
>      recast(scatter) recastci(rcap) scheme(s1color)  title("") ///
>          ci1opts(color(pink*.5) lwidth(medthick))  /// 
>      plot1opts(mcolor(pink))  ///        
>          ci2opts(color(red*.5) lwidth(medthick))  /// 
>      plot2opts(mcolor(red))  ///  
>          ci3opts(color(blue*.5) lwidth(medthick))  /// 
>      plot3opts(mcolor(blue))  ///  
>          ci4opts(color(black*.5) lwidth(medthick))  /// 
>      plot4opts(mcolor(black))  ///  
>      xtitle(Negative Orientation Toward Democracy)  ytitle("Pr(Blank or Spoiled Ballot)")  ///
>          legend(order(5 "VV" 6 "CV{subscript:low}" 7 "CV{subscript:med}" 8 "CV{subscript:high}") rows(1)) aspect(1) ///
>          xlabel(-2 "1" -1 "2" 0 "3" 1 "4" 2 "5") ///
>          name(negative_orient_dem, replace)

  Variables that uniquely identify margins: negative_orient_dem_scale_cent CVscale_Payne

.          
.          
. **
. *Figure 2: Use Graph Editor to change offset for legend's y-axis to 24 and overall y-axis offset to -23
. **
. grc1leg  ignor_disint distrust  negative_orient_dem ///
>         , rows(1) ysize(5.5) xsize(5.5) scale(.7) ycommon graphregion(margin(zero)) scheme(s1color)  

. 
. **
. *Model 5: Use QR decomposition and two integration points to achieve convergence
. **
. meqrlogit would_spoil_or_blank  b0.CVscale_Payne##c.ignor_disint_scale_cent  b0.CVscale_Payne##c.distrust_scale_cent  b0.CVscale_Payne##c.ne
> gative_orient_dem_scale_cent  age_10 educ  GDPpercapita_PPP polity_Bel cpi ignor_disint_scale_mean distrust_scale_mean negative_orient_dem_s
> cale_mean if samp_CVspoil_noUSCAN == 1  || countryandyear: ignor_disint_scale_cent distrust_scale_cent negative_orient_dem_scale_cent,  intp
> oints(2)  difficult   

Refining starting values: 

Iteration 0:   log likelihood = -11386.672  (not concave)
Iteration 1:   log likelihood = -11338.146  (not concave)
Iteration 2:   log likelihood = -11276.064  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -11276.064  (not concave)
Iteration 1:   log likelihood = -11258.592  
Iteration 2:   log likelihood = -11256.527  
Iteration 3:   log likelihood = -11256.442  
Iteration 4:   log likelihood = -11256.441  

Mixed-effects logistic regression               Number of obs      =     41942
Group variable: countryandyear                  Number of groups   =        32

                                                Obs per group: min =       985
                                                               avg =    1310.7
                                                               max =      2382

Integration points =   2                        Wald chi2(23)      =    292.83
Log likelihood = -11256.441                     Prob > chi2        =    0.0000

----------------------------------------------------------------------------------------------------------------
                          would_spoil_or_blank |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------------------------+----------------------------------------------------------------
                                 CVscale_Payne |
                                            1  |   .5330132   .3642048     1.46   0.143     -.180815    1.246841
                                            2  |   .9365951   .3813698     2.46   0.014     .1891241    1.684066
                                            3  |   2.259327   .4208836     5.37   0.000      1.43441    3.084244
                                               |
                       ignor_disint_scale_cent |   .2086357   .1079258     1.93   0.053     -.002895    .4201665
                                               |
       CVscale_Payne#c.ignor_disint_scale_cent |
                                            1  |   .1328304   .1282448     1.04   0.300    -.1185248    .3841856
                                            2  |   .0058674   .1227846     0.05   0.962     -.234786    .2465208
                                            3  |   .1358944   .1196403     1.14   0.256    -.0985964    .3703852
                                               |
                           distrust_scale_cent |  -.0044469   .0681621    -0.07   0.948    -.1380421    .1291484
                                               |
           CVscale_Payne#c.distrust_scale_cent |
                                            1  |   .0714415    .080768     0.88   0.376    -.0868609    .2297438
                                            2  |    .204122   .0784889     2.60   0.009     .0502866    .3579573
                                            3  |   .2257488   .0772263     2.92   0.003      .074388    .3771096
                                               |
                negative_orient_dem_scale_cent |   .0093826   .1199792     0.08   0.938    -.2257723    .2445375
                                               |
CVscale_Payne#c.negative_orient_dem_scale_cent |
                                            1  |  -.0207252   .1417259    -0.15   0.884    -.2985029    .2570525
                                            2  |  -.0371429   .1380398    -0.27   0.788     -.307696    .2334102
                                            3  |     .12618   .1350012     0.93   0.350    -.1384176    .3907776
                                               |
                                        age_10 |  -.1094998   .0129703    -8.44   0.000    -.1349211   -.0840785
                                          educ |   .0640403   .0585083     1.09   0.274    -.0506337    .1787144
                              GDPpercapita_PPP |   .0488375   .0415478     1.18   0.240    -.0325948    .1302697
                                    polity_Bel |   .0230357   .1293594     0.18   0.859     -.230504    .2765754
                                           cpi |   .1803029   .1926205     0.94   0.349    -.1972264    .5578321
                       ignor_disint_scale_mean |   1.209408   .9751368     1.24   0.215    -.7018248    3.120641
                           distrust_scale_mean |   -.798124   .5119024    -1.56   0.119    -1.801434    .2051863
                negative_orient_dem_scale_mean |   1.463325   1.278077     1.14   0.252    -1.041659    3.968309
                                         _cons |  -9.234458    3.37121    -2.74   0.006    -15.84191   -2.627007
----------------------------------------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
countryand~r: Independent    |
               var(ignor_~t) |   .0120922    .007458      .0036101    .0405038
               var(distru~t) |   .0072127    .003336      .0029135    .0178562
               var(negati~t) |   .0185375   .0101387       .006346    .0541499
                  var(_cons) |   .3524414   .0996635      .2024808    .6134652
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(4) =   503.84   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. estat ic

Akaike's information criterion and Bayesian information criterion

-----------------------------------------------------------------------------
       Model |    Obs    ll(null)   ll(model)     df          AIC         BIC
-------------+---------------------------------------------------------------
           . |  41942           .   -11256.44     28     22568.88    22810.92
-----------------------------------------------------------------------------
               Note:  N=Obs used in calculating BIC; see [R] BIC note

. 
. 
. 
. ******                  
. ****Regression Discontinuity Models
. ******
. *create a variable that takes the cutoff to be one year above the cutoff age---that is, set this value to 0, as per the way the rd package w
> orks
. gen age_rd = age-71 if country~="Ecuador"
(15538 missing values generated)

. replace age_rd = age-66 if country=="Ecuador"
(13415 real changes made)

. 
. ***Ignorance and Disinterest    
. *get 25th and 75th percentiles
. sum ignor_disint_scale, detail

          predicted values from FA of Ignorance and
                         Disinterest
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .7944161       .7944161
 5%     1.222061       .7944161
10%     1.622538       .7944161       Obs               95304
25%     2.146604       .7944161       Sum of Wgt.       95304

50%     2.806196                      Mean           2.810067
                        Largest       Std. Dev.      .8816879
75%     3.497653       4.460599
90%     3.988013       4.460599       Variance       .7773735
95%     4.292069       4.460599       Skewness      -.1314053
99%     4.460599       4.460599       Kurtosis       2.418202

. global p25 = r(p25)

. global p75 = r(p75)

. 
. *run an RD model for those low on Ignorance and Disinterest in countries with thresholds.  
. rd  would_spoil_or_blank  age_rd if ignor_disint_scale<$p25  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | country == 
> "Ecuador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 2.4732323; loc Wald Estimate: .07721734
Bandwidth: 4.9464647; loc Wald Estimate: -.03179423
(153352 missing values generated)
(153352 missing values generated)
(153352 missing values generated)
Estimating for bandwidth 2.47323232522771
Estimating for bandwidth 4.94646465045542
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |   .0772173   .1535049     0.50   0.615    -.2236468    .3780815
    lwald200 |  -.0317942     .09009    -0.35   0.724    -.2083674    .1447789
------------------------------------------------------------------------------

. graph save rd_ignor_disint_scale_low, replace
(note: file rd_ignor_disint_scale_low.gph not found)
(file rd_ignor_disint_scale_low.gph saved)

. 
. *run an RD model for those high on Ignorance and Disinterest in countries with thresholds
. rd  would_spoil_or_blank  age_rd if ignor_disint_scale>$p75  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | country == 
> "Ecuador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 2.837819; loc Wald Estimate: -.04975667
Bandwidth: 5.6756379; loc Wald Estimate: -.01577299
(150878 missing values generated)
(150878 missing values generated)
(150878 missing values generated)
Estimating for bandwidth 2.837818973917554
Estimating for bandwidth 5.675637947835108
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.0497567   .1104477    -0.45   0.652    -.2662302    .1667169
    lwald200 |   -.015773   .0644337    -0.24   0.807    -.1420608    .1105148
------------------------------------------------------------------------------

. graph save rd_ignor_disint_scale_high, replace
(note: file rd_ignor_disint_scale_high.gph not found)
(file rd_ignor_disint_scale_high.gph saved)

. 
. 
. ***Distrust in Democratic Institutions and Actors       
. *get 25th and 75th percentiles
. 
. sum distrust_scale, detail

     predicted values from FA of Distrust in Democratic
                   Institutions and Actors
-------------------------------------------------------------
      Percentiles      Smallest
 1%     1.055527       1.055527
 5%     1.656857       1.055527
10%     2.111053       1.055527       Obs              100043
25%      3.16658       1.055527       Sum of Wgt.      100043

50%     4.294191                      Mean           4.377679
                        Largest       Std. Dev.      1.674556
75%     5.646954       7.388687
90%      6.72213       7.388687       Variance       2.804138
95%     7.388687       7.388687       Skewness       .0276049
99%     7.388687       7.388687       Kurtosis       2.213331

. global p25 = r(p25)

. global p75 = r(p75)

. 
. *run an RD model for those low on Distrust in Democratic Institutions and Actors in countries with thresholds.  
. rd  would_spoil_or_blank  age_rd if distrust_scale<$p25  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | country == "Ecu
> ador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 3.3536007; loc Wald Estimate: -.01611638
Bandwidth: 6.7072015; loc Wald Estimate: .01496801
(154395 missing values generated)
(154395 missing values generated)
(154395 missing values generated)
Estimating for bandwidth 3.353600741073913
Estimating for bandwidth 6.707201482147826
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.0161164   .1225387    -0.13   0.895    -.2562877     .224055
    lwald200 |    .014968   .0857018     0.17   0.861    -.1530044    .1829404
------------------------------------------------------------------------------

. graph save rd_distrust_scale_low, replace
(note: file rd_distrust_scale_low.gph not found)
(file rd_distrust_scale_low.gph saved)

. 
. *run an RD model for those high on Distrust in Democratic Institutions and Actors in countries with thresholds
. rd  would_spoil_or_blank  age_rd if distrust_scale>$p75  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | country == "Ecu
> ador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 2.7520997; loc Wald Estimate: -.08526383
Bandwidth: 5.5041995; loc Wald Estimate: -.06906253
(147956 missing values generated)
(147956 missing values generated)
(147956 missing values generated)
Estimating for bandwidth 2.752099731605121
Estimating for bandwidth 5.504199463210242
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.0852638   .1038856    -0.82   0.412    -.2888759    .1183482
    lwald200 |  -.0690625   .0602081    -1.15   0.251    -.1870682    .0489432
------------------------------------------------------------------------------

. graph save rd_distrust_scale_high, replace
(note: file rd_distrust_scale_high.gph not found)
(file rd_distrust_scale_high.gph saved)

. 
. 
. 
. ***Negative Orientations toward Democracy       
. *get 25th and 75th percentiles
. sum negative_orient_dem_scale, detail

      predicted values from FA of Negative Orientations
                      toward Democracy
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .8149203       .8149203
 5%     1.250056       .8149203
10%     1.629841       .8149203       Obs               76552
25%     2.134853       .8149203       Sum of Wgt.       76552

50%     2.824546                      Mean           2.842182
                        Largest       Std. Dev.      .8564864
75%     3.528766       4.468913
90%     3.963901       4.468913       Variance        .733569
95%     4.019251       4.468913       Skewness      -.2215676
99%     4.399036       4.468913       Kurtosis       2.223341

. global p25 = r(p25)

. global p75 = r(p75)

. 
. *run an RD model for those low on Negative Orientations toward Democracy in countries with thresholds. 
. rd  would_spoil_or_blank  age_rd if negative_orient_dem_scale<$p25  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | coun
> try == "Ecuador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 1.7558368; loc Wald Estimate: .05175983
Bandwidth: 3.5116737; loc Wald Estimate: .09184631
(153621 missing values generated)
(153621 missing values generated)
(153621 missing values generated)
Estimating for bandwidth 1.755836847940069
Estimating for bandwidth 3.511673695880138
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |   .0517598   .0786949     0.66   0.511    -.1024794    .2059991
    lwald200 |   .0918463    .098853     0.93   0.353     -.101902    .2855947
------------------------------------------------------------------------------

. graph save rd_negative_orient_dem_scale_low, replace
(note: file rd_negative_orient_dem_scale_low.gph not found)
(file rd_negative_orient_dem_scale_low.gph saved)

. 
. *run an RD model for those high on Negative Orientations toward Democracy in countries with thresholds
. rd  would_spoil_or_blank  age_rd if negative_orient_dem_scale>$p75  & (country == "Peru" | country == "Bolivia" | country == "Brazil" | coun
> try == "Ecuador" | country == "Argentina") , gr noscatter mbw(200)
Two variables specified; treatment is 
assumed to jump from zero to one at Z=0. 

 Assignment variable Z is age_rd
 Treatment variable X_T unspecified
 Outcome variable y is would_spoil_or_blank

Command used for graph: lpoly; Kernel used: triangle (default)
Bandwidth: 2.8157018; loc Wald Estimate: -.12206965
Bandwidth: 5.6314036; loc Wald Estimate: -.0439293
(148739 missing values generated)
(148739 missing values generated)
(148739 missing values generated)
Estimating for bandwidth 2.815701785070762
Estimating for bandwidth 5.631403570141524
------------------------------------------------------------------------------
would_spoi~k |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lwald |  -.1220697   .1003665    -1.22   0.224    -.3187844    .0746451
    lwald200 |  -.0439293   .0597043    -0.74   0.462    -.1609476     .073089
------------------------------------------------------------------------------

. graph save rd_negative_orient_dem_scale_high, replace
(note: file rd_negative_orient_dem_scale_high.gph not found)
(file rd_negative_orient_dem_scale_high.gph saved)

. 
. **
. *Figure 3: Use Graph Editor after combining to improve aesthetics. These graphs correspond to the estimations that double the size of the Im
> bens and Kalyanaraman optimal window (these are appended with "200").
. **
. graph combine ///
>         "rd_ignor_disint_scale_low.gph" ///
>         "rd_ignor_disint_scale_high.gph" ///
>         "rd_distrust_scale_low.gph" ///
>         "rd_distrust_scale_high.gph" ///
>         "rd_negative_orient_dem_scale_low.gph" ///
>         "rd_negative_orient_dem_scale_high.gph" ///
>         , rows(3) ysize(7) xsize(5) iscale(.55) scale(1) xcommon ycommon graphregion(margin(zero)) scheme(s1color)       

.          
. 
.          
. 
end of do-file

. log close
      name:  <unnamed>
       log:  /Users/singh/Google Drive/PLS/Research/Compulsory Voting/Compulsion, Abstention, and Spoiled Ballots/PSRM Replication Materials/P
> SRM Replication Log, Singh.log
  log type:  text
 closed on:  18 Sep 2016, 15:41:33
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